{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example showing the plot_ks_statistic method used by a scikit-learn classifier\n",
    "\n",
    "In this example we'll be generating a KS statistic plot with scikit-learn's `LogisticRegression` classifier to the breast cancer dataset. The `scikitplot.metrics.plot_ks_statistic` takes the ground-truth labels `y_true` and the predicted probabilities `y_probas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import load_breast_cancer as load_data\n",
    "\n",
    "# Import scikit-plot\n",
    "import scikitplot as skplt\n",
    "\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (14, 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "X, y = load_data(return_X_y=True)\n",
    "\n",
    "# Create classifier instance\n",
    "lr = LogisticRegression()\n",
    "\n",
    "# Fit the model\n",
    "lr.fit(X,y)\n",
    "y_probas = lr.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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42Vbd/+xmNbX3zqTrqkr0/649h5HeAAAgFJMmTQrW69ev9xgJcl5IPUj8LnkA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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2497ece9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot!\n",
    "skplt.metrics.plot_ks_statistic(y, y_probas)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
